Implementation and Interpretation of Machine and Deep Learning to Applied Subsurface Geological Problems

Prediction Models Exploiting Well-Log Information

David A. Wood

• Publisher: Elsevier
• Year: 2025
• Topic: Technology & Engineering
• eBook ISBN:
  9780443265112
• Subtopic: Natural Resource Extraction Industry
• Index: Technology & Engineering

Implementation and Interpretation of Machine and Deep Learning to Applied Subsurface Geological Problems: Prediction Models Exploiting Well-Log Information explores machine and deep learning models for subsurface geological prediction problems commonly encountered in applied resource evaluation and reservoir characterization tasks. The book provides insights into how the performance of ML/DL models can be optimized—and sparse datasets of input variables enhanced and/or rescaled—to improve prediction performances. A variety of topics are covered, including regression models to estimate total organic carbon from well-log data, predicting brittleness indexes in tight formation sequences, trapping mechanisms in potential sub-surface carbon storage reservoirs, and more.Each chapter includes its own introduction, summary, and nomenclature sections, along with one or more case studies focused on prediction model implementation related to its topic. - Addresses common applied geological problems focused on machine and deep learning implementation with case studies - Considers regression, classification, and clustering machine learning methods and how to optimize and assess their performance, considering suitable error and accuracy metric - Contrasts the pros and cons of multiple machine and deep learning methods - Includes techniques to improve the identification of geological carbon capture and storage reservoirs, a key part of many energy transition strategies

Table of contents

• Title of Book
• Chapter 1. Overview of artificial intelligence methods and data analysis techniques suitable for subsurface datasets
• Chapter 2. Regression models to estimate total organic carbon (TOC) from well-log data
• Chapter 3. Predicting brittleness indexes in tight formation sequences
• Chapter 4. Classifying lithofacies from well logs using supervised machine learning, cluster, and principal component analysis plus stacking model combinations
• Chapter 5. Permeability, porosity, and water saturation relationships and distributions in complex reservoirs
• Chapter 6. Trapping mechanisms in potential subsurface carbon storage reservoirs and their prediction by machine learning
• Chapter 7. Autocorrelation and accurate picking of formation boundaries using well-log data from multiple wells drilled in complex geological sequences
• Chapter 8. Assessing formation loss of circulation risks with mud-log datasets: resampling and classifying imbalanced datasets
• Chapter 9. Predicting formation fracture characteristics derived from borehole image data with petrophysical well-log variables
• Chapter 10. Quantifying reservoir microfacies characterization using thin-section, scanned, computed tomography, and electron microscope image data
• Chapter 11. Diverse machine learning applications for coal property characterization of coalbed methane and mining resources
• Index